




"""simple docstring"""

from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__magic_name__				     =     logging.get_logger(__name__)






class        SCREAMING_SNAKE_CASE__   (						_SCREAMING_SNAKE_CASE      ):
   snake_case			   =       ["""pixel_values"""]
   def __init__(       self			:				List[str]   ,	SCREAMING_SNAKE_CASE_			:				bool = True   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int] = None   ,	SCREAMING_SNAKE_CASE_			:				PILImageResampling = PILImageResampling.BICUBIC   ,	SCREAMING_SNAKE_CASE_			:				bool = True   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int] = None   ,	SCREAMING_SNAKE_CASE_			:				bool = True   ,	SCREAMING_SNAKE_CASE_			:				Union[int, float] = 1 / 255   ,	SCREAMING_SNAKE_CASE_			:				bool = True   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD   ,	**SCREAMING_SNAKE_CASE_			:				str   ,	):
          super().__init__(**SCREAMING_SNAKE_CASE_							)
          lowerCamelCase__          =						size if size is not None else {'shortest_edge': 224}
          lowerCamelCase__          =						get_size_dict(SCREAMING_SNAKE_CASE_   ,	default_to_square=SCREAMING_SNAKE_CASE_							)
          lowerCamelCase__          =						crop_size if crop_size is not None else {'height': 224, 'width': 224}
          lowerCamelCase__          =						get_size_dict(SCREAMING_SNAKE_CASE_   ,	param_name="""crop_size"""							)

          lowerCamelCase__          =						do_resize
          lowerCamelCase__          =						size
          lowerCamelCase__          =						resample
          lowerCamelCase__          =						do_center_crop
          lowerCamelCase__          =						crop_size
          lowerCamelCase__          =						do_rescale
          lowerCamelCase__          =						rescale_factor
          lowerCamelCase__          =						do_normalize
          lowerCamelCase__          =						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          lowerCamelCase__          =						image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def    __UpperCAmelCase       (       self			:				List[str]   ,	SCREAMING_SNAKE_CASE_			:				np.ndarray   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int]   ,	SCREAMING_SNAKE_CASE_			:				PILImageResampling = PILImageResampling.BICUBIC   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[str, ChannelDimension]] = None   ,	**SCREAMING_SNAKE_CASE_			:				Union[str, Any]   ,	):
          lowerCamelCase__          =						get_size_dict(SCREAMING_SNAKE_CASE_   ,	default_to_square=SCREAMING_SNAKE_CASE_							)
          # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
          if "shortest_edge" in size:
                 lowerCamelCase__          =						int((256 / 224) * size["""shortest_edge"""]							)
                 lowerCamelCase__          =						get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,	size=SCREAMING_SNAKE_CASE_   ,	default_to_square=SCREAMING_SNAKE_CASE_							)
                 lowerCamelCase__          =						{'height': output_size[0], 'width': output_size[1]}
          if "height" not in size_dict or "width" not in size_dict:
                 raise ValueError(
                     f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""							)
          return resize(
              SCREAMING_SNAKE_CASE_   ,	size=(size_dict["""height"""], size_dict["""width"""])   ,	resample=SCREAMING_SNAKE_CASE_   ,	data_format=SCREAMING_SNAKE_CASE_   ,	**SCREAMING_SNAKE_CASE_							)
   def    __UpperCAmelCase       (       self			:				int   ,	SCREAMING_SNAKE_CASE_			:				np.ndarray   ,	SCREAMING_SNAKE_CASE_			:				Dict[str, int]   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[str, ChannelDimension]] = None   ,	**SCREAMING_SNAKE_CASE_			:				Dict   ,	):
          lowerCamelCase__          =						get_size_dict(SCREAMING_SNAKE_CASE_							)
          if "height" not in size or "width" not in size:
                 raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""							)
          return center_crop(SCREAMING_SNAKE_CASE_   ,	size=(size["""height"""], size["""width"""])   ,	data_format=SCREAMING_SNAKE_CASE_   ,	**SCREAMING_SNAKE_CASE_							)
   def    __UpperCAmelCase       (       self			:				Dict   ,	SCREAMING_SNAKE_CASE_			:				np.ndarray   ,	SCREAMING_SNAKE_CASE_			:				Union[int, float]   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[str, ChannelDimension]] = None   ,	**SCREAMING_SNAKE_CASE_			:				List[str]   ,	):
          return rescale(SCREAMING_SNAKE_CASE_   ,	scale=SCREAMING_SNAKE_CASE_   ,	data_format=SCREAMING_SNAKE_CASE_   ,	**SCREAMING_SNAKE_CASE_							)
   def    __UpperCAmelCase       (       self			:				Dict   ,	SCREAMING_SNAKE_CASE_			:				np.ndarray   ,	SCREAMING_SNAKE_CASE_			:				Union[float, List[float]]   ,	SCREAMING_SNAKE_CASE_			:				Union[float, List[float]]   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[str, ChannelDimension]] = None   ,	**SCREAMING_SNAKE_CASE_			:				Dict   ,	):
          return normalize(SCREAMING_SNAKE_CASE_   ,	mean=SCREAMING_SNAKE_CASE_   ,	std=SCREAMING_SNAKE_CASE_   ,	data_format=SCREAMING_SNAKE_CASE_   ,	**SCREAMING_SNAKE_CASE_							)





   def    __UpperCAmelCase       (       self			:				Any   ,	SCREAMING_SNAKE_CASE_			:				ImageInput   ,	SCREAMING_SNAKE_CASE_			:				Optional[bool] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Dict[str, int]] = None   ,	SCREAMING_SNAKE_CASE_			:				PILImageResampling = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[bool] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Dict[str, int]] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[bool] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[float] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[bool] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[float, Iterable[float]]] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Union[float, Iterable[float]]] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[TensorType] = None   ,	SCREAMING_SNAKE_CASE_			:				ChannelDimension = ChannelDimension.FIRST   ,	**SCREAMING_SNAKE_CASE_			:				Tuple   ,	):
          lowerCamelCase__          =						do_resize if do_resize is not None else self.do_resize
          lowerCamelCase__          =						resample if resample is not None else self.resample
          lowerCamelCase__          =						do_center_crop if do_center_crop is not None else self.do_center_crop
          lowerCamelCase__          =						do_rescale if do_rescale is not None else self.do_rescale
          lowerCamelCase__          =						rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCamelCase__          =						do_normalize if do_normalize is not None else self.do_normalize
          lowerCamelCase__          =						image_mean if image_mean is not None else self.image_mean
          lowerCamelCase__          =						image_std if image_std is not None else self.image_std

          lowerCamelCase__          =						size if size is not None else self.size
          lowerCamelCase__          =						get_size_dict(SCREAMING_SNAKE_CASE_   ,	default_to_square=SCREAMING_SNAKE_CASE_							)
          lowerCamelCase__          =						crop_size if crop_size is not None else self.crop_size
          lowerCamelCase__          =						get_size_dict(SCREAMING_SNAKE_CASE_   ,	param_name="""crop_size"""							)

          lowerCamelCase__          =						make_list_of_images(SCREAMING_SNAKE_CASE_							)

          if not valid_images(SCREAMING_SNAKE_CASE_							):
                 raise ValueError(
                     """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                     """torch.Tensor, tf.Tensor or jax.ndarray."""							)

          if do_resize and size is None:
                 raise ValueError("""Size must be specified if do_resize is True."""							)

          if do_center_crop and crop_size is None:
                 raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

          if do_rescale and rescale_factor is None:
                 raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

          if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

          # All transformations expect numpy arrays.
          lowerCamelCase__          =						[to_numpy_array(SCREAMING_SNAKE_CASE_							) for image in images]

          if do_resize:
                 lowerCamelCase__          =						[self.resize(SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_							) for image in images]

          if do_center_crop:
                 lowerCamelCase__          =						[self.center_crop(SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_							) for image in images]

          if do_rescale:
                 lowerCamelCase__          =						[self.rescale(SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_							) for image in images]

          if do_normalize:
                 lowerCamelCase__          =						[self.normalize(SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_							) for image in images]

          lowerCamelCase__          =						[to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_							) for image in images]

          lowerCamelCase__          =						{'pixel_values': images}
          return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,	tensor_type=SCREAMING_SNAKE_CASE_							)














"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"